SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

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Showing 576–600 of 703 papers

TitleStatusHype
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging—0
An Evaluation of Classification and Outlier Detection Algorithms—0
A refined convergence analysis of pDCA_e with applications to simultaneous sparse recovery and outlier detection—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
Sequential Outlier Detection based on Incremental Decision Trees—0
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of EmotionsCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning—0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Outlier Detection for Robust Multi-dimensional Scaling—0
Transformation Autoregressive Networks—0
3D Scanning: A Comprehensive Survey—0
Detecting abnormal events in video using Narrowed Normality Clusters—0
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition—0
Clustering with Outlier Removal—0
Towards Building Affect sensitive Word Distributions—0
Semi-supervised Outlier Detection using Generative And Adversary Framework—0
Automatic support vector data descriptionCode0
LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION—0
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection—0
Multiple testing for outlier detection in functional data—0
Outlier Detection by Consistent Data Selection Method—0
Identifying Outlier Arms in Multi-Armed Bandit—0
Valid Inference Corrected for Outlier RemovalCode0
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified